Rapid detection of chloramphenicol in food using SERS flexible sensor coupled artificial intelligent tools

Abstract

The abuse of antibiotics is causing gradually increases drug-resistant bacterial strains, which pose a threat to economic development and human health around the world. Therefore, surface-enhanced Raman scattering (SERS) active flower-like silver nanoparticles (AgNPs) was used to fabricate flexible paper-based SERS sensor to acquire magnified Raman signal of chloramphenicol in food samples for the development of a suitable prediction model at picogram levels employing artificial intelligence tools. Among the employed artificial intelligent tools, the multivariate scattering correction integrated competitive adaptive weighted-partial least squares (MSC-CARS-PLS) model showed best prediction efficiency over the concentration range 102 to 10−5 μg/mL with correlation coefficient of test = 0.9635, residual predictive deviation = 3.6686 and a limit of detection = 10−5 μg/mL. The recovery results range from 90 to 102% in real sample analysis and RSD was recorded 3.3% suggested that proposed sensor was rapid, reproducible and reliable for predicting CAP residue in food samples.